人工智能（AI）最近经历了复兴，在视觉，语言，控制和决策等关键领域取得了重大进展。 这部分是由于廉价的数据和廉价的计算资源，它们符合深度学习的自然优势。 然而，在许多不同的压力下发展起来的许多人类智能的定义特征，对于当前的方法仍然遥不可及。 特别是，从婴儿时代起就超越人类的经验进行概括，对于现代AI仍然是一个巨大的挑战。
以下是部分立场文件，部分审查和部分统一。 我们认为，组合泛化必须是AI实现类人能力的头等大事，而结构化表示和计算是实现此目标的关键。 就像生物学合作地利用自然与养育一样，我们拒绝在“手工工程”学习与“端对端”学习之间进行错误的选择，而是主张一种从其互补优势中受益的方法。 我们探索在深度学习架构中使用关系归纳偏差如何促进对实体，关系和组成它们的规则的了解。 我们为AI工具包提供了一个新的构建模块，该模块具有强大的关系归纳图网络，该网络概括并扩展了在图上运行的神经网络的各种方法，并为操作结构化知识和产生结构化行为提供了直接接口。 我们讨论图网络如何支持关系推理和组合泛化，为更复杂，可解释和灵活的推理模式奠定基础。 作为本文的补充，我们还发布了一个用于构建图形网络的开源软件库，并演示了如何在实践中使用它们。
Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one’s experiences|a hallmark of human intelligence from infancy|remains a formidable challenge for modern AI.
The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between \hand-engineering" and \end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias|the graph network|which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have also released an open-source software library for building graph networks, with demonstrations of how to use them in practice.